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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 11
Atmos. Chem. Phys., 11, 5365–5382, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 11, 5365–5382, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 09 Jun 2011

Research article | 09 Jun 2011

Bayesian statistical modeling of spatially correlated error structure in atmospheric tracer inverse analysis

C. Mukherjee1, P. S. Kasibhatla2, and M. West1 C. Mukherjee et al.
  • 1Department of Statistical Science, Duke University, Durham, NC, USA
  • 2Nicholas School of the Environment, Duke University, Durham, NC, USA

Abstract. We present and discuss the use of Bayesian modeling and computational methods for atmospheric chemistry inverse analyses that incorporate evaluation of spatial structure in model-data residuals. Motivated by problems of refining bottom-up estimates of source/sink fluxes of trace gas and aerosols based on satellite retrievals of atmospheric chemical concentrations, we address the need for formal modeling of spatial residual error structure in global scale inversion models. We do this using analytically and computationally tractable conditional autoregressive (CAR) spatial models as components of a global inversion framework. We develop Markov chain Monte Carlo methods to explore and fit these spatial structures in an overall statistical framework that simultaneously estimates source fluxes. Additional aspects of the study extend the statistical framework to utilize priors on source fluxes in a physically realistic manner, and to formally address and deal with missing data in satellite retrievals. We demonstrate the analysis in the context of inferring carbon monoxide (CO) sources constrained by satellite retrievals of column CO from the Measurement of Pollution in the Troposphere (MOPITT) instrument on the TERRA satellite, paying special attention to evaluating performance of the inverse approach using various statistical diagnostic metrics. This is developed using synthetic data generated to resemble MOPITT data to define a proof-of-concept and model assessment, and then in analysis of real MOPITT data. These studies demonstrate the ability of these simple spatial models to substantially improve over standard non-spatial models in terms of statistical fit, ability to recover sources in synthetic examples, and predictive match with real data.

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